Real-Time Observation of Membrane Electrode Water Content and Diagnosis

The dynamic operating conditions of automotive fuel cells are highly complex. Achieving efficient and durable operation necessitates accurate monitoring of membrane electrode water content, thereby enabling fault diagnosis for membrane drying and flooding. While numerous studies have explored state observation and fault diagnosis, the majority focus on high-frequency impedance measurement and membrane water content estimation, which can successfully identify membrane drying but are less effective in detecting flooding during high current density operation. In response, this work introduces a new internal state observer based on a sliding mode algorithm for the membrane electrode and an advanced fault diagnosis method for both membrane drying and flooding. The proposed algorithm, based on a high-precision mass transport model, leverages high-frequency impedance and humidity sensor data to estimate membrane water content and liquid water saturation in the cathode catalyst layer. This methodology permits precise internal state identification across a range of operating conditions and enables robust diagnosis of drying and flooding faults. Experimental results confirm the proposed observer achieves a diagnostic accuracy of 91.67%, underscoring its potential for future implementation.